Scale Reliability Calculator

Work out scale reliability instantly with clear inputs, formula shown and shareable results.

About Scale Reliability Calculator

The standardised form of Cronbach's alpha is k.rbar/(1 + (k-1)rbar), so reliability rises with both the number of items and their average intercorrelation. Ten items averaging 0.45 give alpha 0.891 - good internal consistency. Only four such items would be needed to reach 0.80, which is why long scales are often unnecessary.

Formulas

Standardised alpha

alpha = k x rbar / (1 + (k - 1) x rbar)

Items required

k = target(1 - rbar) / (rbar(1 - target))

Worked example

With the default values below, Scale Reliability Calculator returns Cronbach's Alpha of 0.8911. Change any input above and every figure here is recalculated in your browser.

Inputs used

Number of itemsminimum 2: 10
Average inter-item correlationaccepts -0.99–0.99: 0.45

Results

Cronbach's Alpha: 0.8911
Items Needed For Alpha Of 0.80: 5
Interpretation: Good internal consistency
Alpha With Five More Similar Items: 0.9247

Step by step

1Values used
2Standardised alpha
3Items required
4Cronbach's Alpha= 0.8911
5Items Needed For Alpha Of 0.80= 5
6Interpretation= Good internal consistency
7Alpha With Five More Similar Items= 0.9247

Assumptions and limits

The result is derived only from the 2 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Can alpha be too high?

Above about 0.95 it usually means items are near-duplicates, which wastes respondent time without adding information.

Does high alpha prove the scale measures one thing?

No. Alpha rises with item count regardless of dimensionality, so factor analysis is needed to establish unidimensionality.